Devops Intermediate Premium

Machine Learning Ops (MLOps) Virtual Internship

In this MLOps virtual internship, students will learn to develop and deploy machine learning models using DevOps principles and tools. They will gain hands-on experience with model versioning, continuous integration and deployment (CI/CD), and infrastructure automation. By the end of the internship, students will be able to build and maintain robust, scalable, and reproducible ML pipelines.

weeks
8 tasks
0 enrolled
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Track price: $49.00

Track Overview

This track provides hands-on experience and real-world projects to build your skills.

Tasks & Milestones

Task 1: Understand MLOps Concepts

Intermediate

In this task, students will explore the fundamental concepts of MLOps, including model versioning, model deployment, and infrastructure management.

8 hours

Task 2: Explore MLOps Tools and Frameworks

Intermediate

In this task, students will research and compare various MLOps tools and frameworks, evaluating their features and use cases.

12 hours

Task 1: Version Control for ML Models

Intermediate

In this task, students will learn how to version control their machine learning models using Git, including best practices for model file management and model metadata tracking.

10 hours

Task 2: Model Lineage and Provenance Tracking

Intermediate

In this task, students will explore techniques for tracking the lineage and provenance of their machine learning models, ensuring transparency and auditability.

12 hours

Task 1: Implement CI/CD for Model Training and Testing

Intermediate

In this task, students will set up a CI/CD pipeline for automatically training and testing their machine learning models.

16 hours

Task 2: Automate Model Deployment to Production

Intermediate

In this task, students will learn how to automate the deployment of their machine learning models to production environments.

20 hours

Task 1: Provision ML Infrastructure using Terraform

Intermediate

In this task, students will use Terraform to provision the infrastructure required for their machine learning pipelines, including cloud resources and supporting services.

18 hours

Task 2: Manage ML Infrastructure using Ansible

Intermediate

In this task, students will use Ansible to manage and maintain the infrastructure required for their machine learning pipelines, ensuring consistency and scalability.

16 hours

Prerequisites

  • • Basic understanding of machine learning concepts
  • • Experience with Python programming
  • • Familiarity with Git and GitHub

Certificate

Certificate of Completion

Earn a certificate upon successful completion